sgouda0412/orvp

7th place submission for Optiver Realized Volatility Prediction competition on Kaggle

Optiver Realized Volatility Prediction model

This is the README file for the 7th place solution in the ORVP Kaggle competition. All notebooks were ran exclusively on Kaggle kernels, all .py files were also ran on Kaggle kernels, but also tested on the following platform:

Intel M-5Y10c dual core 0.80GHz CPU, Intel HD Graphics 5300 GPU, 8 GB RAM, Manjaro Linux - Kernel 5.10.89-1 OS.

This directory contains two models: the model used as a final submission to the competition, scoring 0.20013 RMSPE on the public dataset, and a second, more practical model that could be used in a real life scenario, scoring 0.22955 RMSPE on the public dataset.

The first model is located in the main_model folder. I don't expect the host to actually run the first model in production, so it doesn't contain a serialized version of the trained model, a list of dependencies, settings, directory structure etc. You can try the code for yourself by importing the .ipynb file in a Kaggle notebook, importing the competition data and executing it.

The second model is located in the simple_model folder. It contains all files described in section B of the winning model documentation guidelines. Read entry_points.md for information on how to run the model.

Files

README.md: This file. Kaggle_ORVP_report.pdf: Report as outlined in "Section A -- Model Summary" of the winning model documentation guidelines. directory_structure.txt Directory structure file, as described in section B5. main_model: Directory containing the following notebooks for the main model:

• optiver-realized-second-submission.ipynb: Code for pre-processing features, training models and outputting results of the submitted version (0.20013 final score). It would not yield any useful results in production as is.
• feature-selection.ipynb: Code used for feature selection, using pre-processed features extracted from the file above.

simple_model: Directory containing the following code for the simple model:

• feature_processor.py, train.py and inference.py: Model files required to train the model and generate predictions. See entry_points.md for usage.
• entry_points.md: List of commands used to run the model.
• feature-selection.ipynb: Notebook used for feature selection. Downloads BarutaSHAP from pip as it was intended for use on Kaggle kernels.
• requirements.txt: Requirements file.
• SETTINGS.json: Settings file to define paths described in entry_points.md
• submission.csv: Sample output file.
• orvp-features.parquet: Processed features for training.
• model folder: Used to store serialized version(s) of the LightGBM model. Can only contain .pickle files representing LightGBM "model" objects.

data folder: Used to store raw data. You can extract the competition dataset there to test the model.